Blood culture positive sepsis in England, 2017-2018: epidemiological assessment of the Commissioning for Quality and Innovation (CQUIN) sepsis indicator

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This study describes the epidemiology of sepsis in emergency department attendees across England by analysing a unique multi-site linked dataset to inform approaches to strengthen surveillance and understanding of clinical outcomes. Methods An existent study dataset was utilised comprising a sample of paediatric and adult emergency admissions screened for sepsis in the Commissioning for Quality and Innovation (CQUIN) program in the 2017/18 financial year linked to Hospital Episode Statistics and Office for National Statistics death registrations. This was linked to the United Kingdom Health Security Agency’s Second-Generation Surveillance System for microbiological data; descriptive analyses were conducted to characterise sepsis screen positives and negatives in CQUIN, including demographic characteristics, clinical presentations, microbiological profiles, and clinical outcomes. Results Of the 4027 sepsis-screened emergency admissions included, 2454 (60.9%) were sepsis screen positive under the CQUIN indicator. Only 11.2% had a positive blood culture taken within 2 days of hospital admission. Blood culture positivity rates were 15.2% for sepsis screen positive and 5.1% for screen negatives in CQUIN. Monomicrobial episodes predominated (86.5%), with Escherichia coli and Staphylococcus species being the most isolated bacteria. The study showed a case fatality rate of sepsis of 17.1% (420/2454) but revealed no significant difference in all-cause 30-day mortality between sepsis screen positives in CQUIN with and without positive blood cultures. However, sepsis screen positives in CQUIN with a focal site of infection code were more likely to have positive blood cultures, except for respiratory infections. Conclusions This study provides novel insights into the epidemiology of sepsis screening in emergency departments across England, highlighting variability in blood culture positivity rates and microbial profiles. The findings underscore the importance of enhanced surveillance strategies, optimised screening protocols, tailored antimicrobial stewardship practices, and quality improvement initiatives to optimise sepsis management and outcomes. Systemic approaches are needed to address knowledge gaps and inform evidence-based interventions for sepsis care. Sepsis blood culture bacteraemia surveillance CQUIN Figures Figure 1 Introduction Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection ( 1 ). Sepsis is a significant clinical and public health concern due to its high associated mortality, healthcare costs and long term physical, psychological, and cognitive sequelae ( 2 ). In 2022, sepsis was the reported cause of 3,770 deaths in England and Wales and noted on death certification as being an underlying or contributory factor in a further 25,542 deaths ( 3 ). Sepsis has a hospital mortality rate of around one-third (36%) ( 4 – 7 ). Rapid identification of patients with sepsis is critical for targeted clinical management ( 8 ). It can be clinically screened for using scoring systems such as National Early Warning Score (NEWS2) and others ( 9 ). However, each tool has its own limitations, with variations in clinical coding, case definitions and use in clinical practice. Additionally, as they do not identify the causative organism, they are unable to guide appropriate choice of antibiotics and patients must first be treated empirically ( 10 ). Therefore, blood culture remains the most important microbiological investigation in the management of sepsis, allowing identification of the responsible organism and direct targeted investigations for underlying source unless this is clinically evident ( 11 ). As such, blood cultures drawn before administration of broad-spectrum antibiotics and within 3 hours of a sepsis diagnosis forms a key component of the Surviving Sepsis Campaign care bundle ( 12 , 13 ). There is currently no dedicated surveillance system for sepsis in England, limiting our ability to quantify sepsis burden and monitor efforts in reducing its occurrence and impact. Current estimates of sepsis incidence in England are primarily derived from bespoke analysis of routine administrative data, such as Hospital Episode Statistics (HES) using International Classification of Disease (ICD-10) codes to record clinical diagnoses ( 14 ). This system, which contains details on all admissions at National Health Service (NHS) hospitals in England, is primarily designed to provide financial reimbursement for hospitals, and therefore its secondary use for surveillance has limitations, including imprecise clinical coding of infection ( 2 , 15 , 16 ). Additionally, it is often difficult to translate complex conditions, such as sepsis, into a single ICD-10 code, and the use of these codes have previously been found to have low specificity and sensitivity ( 16 ). While UK Health Security Agency (UKHSA, formerly Public Health England [PHE]) has developed a national surveillance system to automate the reporting of positive blood isolates from diagnostic laboratories in England, the reason for the blood culture collection is not routinely recorded or collated. In 2015 Commissioning for Quality and Innovation (CQUIN) indicators on sepsis were introduced to incentivise its screening in patients that arrive in emergency departments (EDs) and acute inpatient settings ( 17 ). The CQUIN indicator measured the percentage of adult and child patients arriving in hospital as emergency admissions or on acute in-patient wards who met the NICE criteria for sepsis screening and were screened for sepsis, including review by a senior clinical decision maker, though precise criteria for screening varied by local protocol ( 18 ). From 2018, all acute and emergency units were recommended to use the NEWS2 for clinical screening of acutely ill patients; prior to this, local guidance varied widely on the criteria applied for screening patients for sepsis and case definitions used ( 18 ). A version of the CQUIN sepsis indicator ran until 2019. The aim was to encourage timely identification and antibiotic treatment, to improve patient outcomes and reduce sepsis-associated mortality and morbidity. The data collected on the implementation of the CQUIN sepsis indicator in hospitals provides a novel data source with the potential to further our understanding of sepsis epidemiology in England. It allows a means to further understand and validate other routine data sources, such as HES, for identifying sepsis. Additionally, there is currently limited information available on the pathogens identified in the people treated for sepsis in England. This paper aims to combine data from CQUIN and HES with UKHSA’s Second Generation Surveillance System (SGSS) for positive microbiology results, to describe the bacterial and fungal organisms isolated from blood cultures in a sample of patients screened for sepsis in CQUIN and attempt to identify associations between positive bloodstream isolates and clinical outcomes. Methods Data collection and management An existing dataset held by UKHSA was obtained and further supplemented for this study. The original study dataset contained information collated from three data sources: CQUIN, HES and Office for National Statistics (ONS), which had been previously created to assess the feasibility of using these data for sepsis surveillance in England ( 3 , 14 , 17 ). Dataset curation is detailed elsewhere ( 19 ). The dataset contains a random sample of CQUIN sepsis screened admission records from 30 Hospital Trusts, including NHS number, demographic data, and clinical admission data, as well as 30-day mortality result from the ONS linkage. Only individuals screened in, and admitted from, EDs into hospital were included in this dataset. Each record relates to an admission super spell: a continuous period of inpatient care. Persons could be admitted on multiple occasions over the study period. Trusts’ submissions varied in size and the study used random selection of data submitted by each trust stratified by CQUIN sepsis screen result (positive/negative) with 110 randomly selected in each stratum or, if fewer than 110, all available in that stratum. In this study, this dataset was further enhanced through deterministic linkage by NHS number with routine laboratory surveillance data to obtain microbiological results, to create the full linked study dataset (Fig. 1 ). First, any records not coded as emergency admissions (i.e., admission to hospital not coming through the emergency department such as elective surgery and maternity) in hospital admission method in HES were dropped. Then, positive isolates were obtained from the Communicable Disease Record (CDR) data feed of the SGSS held by the UKHSA. The CDR feed was queried for all positive blood isolate records (bacteria and fungi) between 25 March 2017 and 05 April 2018 (to allow for a 5-day lag and lead time on specimen date). Isolate records were grouped into rolling 14-day episodes by unique patient identifier (based on NHS number, hospital number, specimen date and date of birth). It should be noted that all positive blood isolates were used, without the ability to differentiate on clinical significance or contamination. Episodes were retained where the first isolate record fell within 2 days before and after the patient’s hospital admission ‘super-spell’, defined in HES as a continuous admission period including transfer to receiving hospitals if applicable, as the infection was considered the same episode of hospitalisation and defined as ‘community-onset’. Any cases with a first positive blood culture taken three or more days after the admission the hospital admission were considered ‘hospital-acquired’ and were not included in the descriptive analysis. Monomicrobial episodes were classified as those with isolates from a single species within the 14-day period. Polymicrobial episodes were classified as those with multiple isolates of different species, from one or more blood cultures, within the 14-day episode. The following definitions were employed: Sepsis screen in CQUIN (positive or negative) : Persons presenting at EDs with suspected sepsis (based on local protocols) who underwent a sepsis screen as part of the CQUIN sepsis indicator and screened either positive or negative for sepsis. Sepsis in primary HES code (present or absent) : Persons included in ( 1 ), and who had (present) vs those who did not have (absent) a primary diagnosis of sepsis in the first Finished Consultant Episode from HES (main condition treated during an episode of care). ICD-10 codes A40.x and A41.x were used in this analysis, as used elsewhere ( 15 , 16 ). Positive blood culture (present or absent) : Persons included in ( 1 ), and who had (present) vs those who did not have (absent) a positive blood culture record present within UKHSA’s SGSS CDR feed within 2 days before and after hospital admission. 30-day mortality was determined as a person included in ( 1 ) that had an all-cause death recorded in ONS in the thirty days following the date of their admission. Data analysis The linked dataset was managed, cleaned, and analysed in R version 3.5.0. Descriptive analyses were undertaken on presence of corresponding positive blood culture results by demographic, admission and clinical variables (obtained from CQUIN, HES, ONS and/or SGSS), and outcome (obtained from SGSS or ONS). The primary outcome was detection of any bacteria or fungi in blood cultures. Categorical variables were compared using chi-squared test and continuous variables compared by Mann-Whitney test. Results Table 1: Characteristics and outcomes of sepsis screen positives in CQUIN emergency admissions by blood culture results. Positive blood culture Sepsis screen positives in CQUIN (n=2454) Presence (n=333) Absence (n=2121) p-value Demographic characteristics, n (%) Sex Male Female 1240 1214 190 (15.3%) 143 (11.8%) 1050 1071 0.012 Ethnicity White All other ethnic groups combined^ Unknown 2159 147 148 291 (13.5%) 21 (14.2%) 21 (14.3%) 1868 127 126 0.938 Age (years), median (IQR) 73 (59-83) 75 (64-84) 73 (58 – 83) 0.008 Age group 0-17 18-49 50-69 70-79 80+ 174 260 605 572 843 19 (10.9%) 20 (7.7%) 82 (13.6%) 82 (14.3%) 130 (15.4%) 155 240 523 490 713 0.022 Primary HES code, n (%) Sepsis Present Absent 665 1789 159 (23.9%) 174 (9.7%) 506 1615 <0.001 Clinical characteristics – site of infection*, n (%) Respiratory Yes No 1220 1234 127 (10.4%) 206 (16.7%) 1093 1028 <0.001 Genitourinary Yes No 368 2086 98 (26.6%) 235 (11.3%) 270 1851 <0.001 Gastrointestinal Yes No 78 2376 18 (23.1%) 315 (13.3%) 60 2061 0.020 Skin and subcutaneous tissue Yes No 165 2289 36 (21.8%) 297 (13.0%) 129 1992 0.002 Other Yes No 115 2339 40 (34.8%) 293 (12.5%) 75 2046 <0.001 Complications Septic shock/SIRS with organ dysfunction Yes No 118 2336 26 (22.0%) 307 (13.1%) 92 2029 0.009 30-day mortality^, n (%) Yes 420 65 (15.5%) 355 No 2034 268 (13.2%) 1766 0.240 Figure legend: *as reported in HES; ^as reported in ONS Description of linked study dataset Of all the 4027 records (CQUIN screen positive or negative), 453 (11.2%) linked to a positive blood isolate within the 2-days before and after their hospital admission (Figure 1): 373 (82.3%) were sepsis screen positive and 80 (17.6%) were sepsis screen negative in CQUIN (Figure 1). Heterogeneity was observed in blood culture positivity rates by CQUIN and HES classifications (Table 2). There was no evidence for a statistically significant difference in 30-day mortality between those with and without presence of positive blood culture (15.5% vs 13.2%, p=0.240) (Table 1). Blood culture positivity by sepsis classification Sepsis screen positives in CQUIN had higher rates of positive blood culture as compared to sepsis screen negatives in CQUIN (15.2% vs 5.1%, p <0.001). In addition, heterogeneity was seen within these groups when stratified by presence or absence of sepsis in a primary HES code (Table 2). Table 2: Sepsis screen positives in CQUIN with at least one positive blood culture during admission Positive blood culture* Presence (%) Absence (%) Total Sepsis screen in CQUIN Sepsis in primary HES code Positive All 373 (15.2) 2081 (84.8) 2454 Present 168 (25.3) 497 (74.7) 665 Absent 205 (11.5) 1584 (88.5) 1789 Negative All 80 (5.1) 1493 (94.9) 1573 Present 18 (14.1) 110 (85.9) 128 Absent 62 (4.3) 1383 (95.7) 1445 Total All 453 (11.2) 3574 (88.8) 4027 Figure legend: At least one culture result available in SGSS. S plit by presence/absence of sepsis in a primary HES code (n=4027). Note: *as reported in UKHSA’s SGSS Microbiological characteristics by sepsis classification Of the 333 sepsis screen positives in CQUIN with a positive blood culture within two days of hospital admission, 288 (86.5%) were monomicrobial episodes and 45 (13.5%) were polymicrobial. Of the 288 sepsis screen positives in CQUIN with monomicrobial infection, all had bacterial infections (supplementary material). Of these, 140 (48.6%) also had sepsis recorded in a primary HES code. The top five genera identified were Escherichia (89, 31%) , Staphylococcus (72, 25%) , Streptococcus (53, 18%) , Klebsiella (16, 6%) and Pseudomonas (14, 5%) . The top five species were Escherichia coli (89, 31%), coagulase-negative Staphylococcus (53, 18%), Streptococcus pneumoniae (22, 8%), Staphylococcus aureus (18, 6%) and Klebsiella pneumoniae (16, 6%) (Table 3). As the numbers of cases were too small to determine pathogen-specific 30-day all-cause mortality, bacteria were grouped into Gram positive or negative. For the 288 records with monomicrobial episodes, there was no evidence of a difference in 30-day mortality amongst those infected with Gram positive vs Gram negative bacteria (16% vs 20% respectively, p=0.57). Table 3: Sepsis screen positives in CQUIN with positive blood culture for monomicrobial episodes Sepsis in primary HES code Organism species name Absent Present Total Escherichia coli 37 (25.0%) 52 (37.1%) 89 (30.9%) Staphylococcus ( coagulase negative) 35 (23.6%) 18 (12.9%) 53 (18.4%) Streptococcus pneumoniae 10 (6.8%) 12 (8.6%) 22 (7.6%) Staphylococcus aureus 11 (7.4%) 7 (5.0%) 18 (6.2%) Klebsiella pneumoniae 6 (4.1%) 10 (7.1%) 16 (5.6%) Pseudomonas aeruginosa 5 (3.4%) 8 (5.7%) 13 (4.5%) Streptococcus group A 6 (4.1%) 2 (1.4%) 8 (2.8%) Enterococcus faecalis 4 (2.7%) 3 (2.1%) 7 (2.4%) Streptococcus alpha and non-haemolytic 2 (1.4%) 5 (3.6%) 7 (2.4%) Micrococcus luteus (sarcina) 3 (2.0%) 2 (1.4%) 5 (1.7%) Figure legend: results split by presence/absence of sepsis in primary HES code and species (Top 10, n=288) Of the 148 (51.4%) blood culture positive (monomicrobial) sepsis screen positives in CQUIN that did not have sepsis in a primary HES code, the most common HES primary infection ICD-10 codes were for “Lobar pneumonia, unspecified organism” (12, 13%), “Pneumonia, unspecified organism” (9, 6%), Pneumonia due to Streptococcus pneumoniae (7, 5%), “Pneumonitis due to inhalation of food and vomit” (7, 5%), “Cellulitis and acute lymphangitis of other parts of limb” (7, 5%) and “Urinary tract infection, site not specified” (7, 5%). Of the 45 sepsis screen positives in CQUIN with positive blood culture results from polymicrobial episodes, all had bacterial infections and made up a total of 98 species isolates (Table 4). Of these, eight cases had three different organisms within one episode, 36 cases had two different organisms within one episode and one case had one organism in their first episode and one (different) organism in their second episode (within the same admission). Of these 98 isolates, the most observed species were Escherichia coli (16, 16%), coagulase-negative Staphylococcus (15, 15%), coliforms (8, 8%), Enterococcus faecalis (4, 4%), Proteus mirabilis (4, 4%) and Staphylococcus aureus (4, 4%). None of the sepsis screen negatives in CQUIN had a polymicrobial episode. Table 4: Sepsis screen positives in CQUIN with positive blood culture for polymicrobial episodes (Top 10) Organism species name Total Escherichia coli 16 (16.3%) Staphylococcus (coagulase negative) 15 (15.3%) Coliform 8 (8.2%) Enterococcus faecalis 4 (4.1%) Proteus Mirabilis 4 (4.1%) Staphylococcus aureus 4 (4.1%) Enterococcus faecium 3 (3.1%) Klebsiella pneumoniae 3 (3.1%) Streptococcus alpha and non-haemolytic 3 (3.1%) Bacteroides fragilis 2 (2.0%) Figure legend: (n=45 records with 98 isolates) There was evidence to suggest that sepsis screen positives in CQUIN that had a site of infection code in HES (such as respiratory) were more likely to have presence of a positive blood culture (p values ranging from p=0.020 to p<0.001) (Table 1), except for respiratory infection where they were less likely to have a presence of positive blood culture (p<0.001) (Table 1).Being male or over 50 years of age were also predictors for presence of a positive blood isolate, as was having sepsis as HES primary code. When looking specifically at sepsis screen positives in CQUIN recorded with respiratory tract as site of infection (n=115), 43 (37.4%) also had sepsis in a primary HES code. The most common species were coagulase negative Staphylococcus (31, 27%), Escherichia coli (21, 18%) and Streptococcus pneumoniae (17, 15%). For records coded with a respiratory site of infection, there was no evidence for a difference in 30-day mortality amongst those infected with Gram positive or negative bacteria, respectively (29% v 18%, p=0.274). Discussion In this study, we found that of patient admission records with sepsis screens in CQUIN in our linked study dataset, only 11.2% had a positive blood culture taken within 2 days of hospital admission, though note that SGSS only includes positive culture results. Positivity rate varied by both CQUIN and HES sepsis classifications; for instance, sepsis screen positives in CQUIN had higher positivity rate (15.2%) than sepsis screen negatives in CQUIN (5.1%). The positivity rate is lower than previous studies, such as by Bernard et al who observed 32%, Phau et al with 42%, and Panday et al with ~43% positivity rates (20-22). However, these studies focused on cases defined as severe sepsis or using pre-hospital diagnostic criteria rather than the screening of all potential cases of sepsis within a hospital setting, where sensitivity for sepsis may be prioritised clinically (20-22). We found that sepsis CQUIN screen positives with a noted site of infection code in HES were more likely to have a positive blood culture, except for respiratory disease who were less likely to have a positive blood culture. This may be due to greater clinical certainty of the differential diagnosis when a clear source of infection is observed, except in respiratory infections who may be less likely to become bacteraemic (23). Most of the blood culture positive episodes identified were monomicrobial, with the most common organisms E. coli (~30%) and coagulase negative Staphylococcus (~18%), though note that many of these are likely to be contaminants (24) . There was no evidence for a statistically significant difference in 30-day mortality between those with and without presence of positive blood culture. This is in line with Sigakis et al , who found positive culture was not independently associated with mortality (OR 1.01, 95% CI 0.81 – 1.26) and Yang et al , who found positive blood culture was not associated with either in-hospital mortality or 60-day mortality but was associated with increased length of stay in hospital and in Intensive Care Units (p=0.007 and p=0.016, respectively) (25, 26). Conversely, this differs from findings by Panday et al who found a significant difference in 28-day mortality between culture-positive and culture-negative patient groups (RR 1.43, 95% CI 1.11 – 1.83) (22). These differences may underscore the importance of independent factors on risk of mortality in sepsis, and the need for further research in this area. This paper uses a unique multi-site dataset obtained from the CQUIN sepsis indicator, further enhanced by linking together multiple data sources from HES, ONS, and SGSS, allowing an overview of emergency admissions screened for sepsis in CQUIN in England in 2017-2018. This allows for a novel exploration of both the CQUIN sepsis indicator and validation of HES for obtaining information on sepsis, together with detailed description on individual characteristics and main causative bacteria. There are several limitations to this study which should be considered. Importantly, we did not exclude any positive culture results which may not have been clinically significant, such as those that may be due to contamination. As such, we may have over-estimated the true rate of positive blood cultures that caused the sepsis episode and impacted the sensitivity of our analysis to observe clinically significant relationships (27). As there was no single definition of sepsis being stipulated in CQUIN, there may have been some heterogeneity in those eligible for screening as part of CQUIN across trusts, which was not possible to assess in this study: as such, we describe individuals throughout the paper as “sepsis screen positive/negative in CQUIN” to emphasise this. UKHSA’s laboratory surveillance system (SGSS) only contains positive blood cultures: as such, for those with an absence of positive blood culture result, it is not possible to ascertain whether this is due to a negative result, perhaps due to preceding antibiotic administration or other factor (28), or to the absence of any culture being taken. By considering all-cause 30-day mortality we may have included deaths due to causes other than sepsis. It was not possible to investigate pathogen-specific 30-day mortality due to low numbers: as such, we categorised organisms into two groups, Gram positive and negative, and compared mortality rates between them. However, the utility of this approach is limited due to the heterogeneity of clinical presentations of organisms within these groups (more details on risk factors associated with mortality can be found in Robinson et al (19)). Finally, administrative systems such as HES and ONS which have their own limitations, such as time of coding relative to screening and diagnosis, and low specificity of coding, which impedes ability to compare these datasets (15). Conclusions Our paper highlights the importance of a multi-faceted approach to the identification of sepsis: although positive blood-cultures remain the clinical “gold standard” in the identification of bacteraemia, other additional markers of sepsis need to be considered as part of hospital admission screening protocols, for prognosis and surveillance. The finding of only 11.2% of admissions having a positive blood culture taken within 2 days of hospital admission is particularly significant here, reinforcing that other markers must play a role in guiding screening, management, and surveillance. There is significant local variation in the application of sepsis screening tools: the development of a clinically optimised sepsis-identification pathway, validated across a range of geographic locales and demographic cohorts, is a logical next step towards improving the early identification of sepsis in England, and ultimately reducing 30-day mortality and the societal costs associated with sepsis. Declarations Ethics approval and consent to participate This project involved analysis of a patient-level pseudonymised extract from a national linked dataset. Consent to participate was not required. The national linked dataset was approved by the PHE Research Ethics and Governance Group (REGG) on the 25/05/2018, approval number NR012 and by the PHE National Infection Service Caldicott Review Panel on the 15/06/2018. Consent for publication Not applicable Availability of data and materials The dataset analysed during the current study is not publicly available due to inclusion of patient-level pseudonymised data. Data may be available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol, in partnership with UK Health Security Agency (UKHSA). The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care, or UKHSA. JH holds an NIHR Academic Clinical Fellowship (ACF-2022-13-013). Authors’ contributions IO, SP, TL, AB, CB, RH and SH designed the study protocol. SP obtained ethical approval and participated in data acquisition and linkage. RM performed statistical analyses and drafted the initial manuscript. RR and AC provided support on statistical analyses and TL, CB, and RH with interpretation of results. All authors helped with manuscript revisions. All authors read and approved the final version of the manuscript for publication. Acknowledgements RR and IO acknowledge support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol References Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). Jama. 2016;315(8):801-10. Cohen J, Vincent J-L, Adhikari NK, Machado FR, Angus DC, Calandra T, et al. Sepsis: a roadmap for future research. The Lancet infectious diseases. 2015;15(5):581-614. Office for National Statistics. Deaths from sepsis in the UK 2001 to 2022, 2023 [Accessed 16/08/2024]. 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Cheng MP, Stenstrom R, Paquette K, Stabler SN, Akhter M, Davidson AC, et al. Blood culture results before and after antimicrobial administration in patients with severe manifestations of sepsis: a diagnostic study. Annals of internal medicine. 2019;171(8):547-54. Additional Declarations No competing interests reported. Supplementary Files Bloodculturepositivesepsispapersupplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 26 Sep, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 26 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 15 Jun, 2025 Reviews received at journal 05 Dec, 2024 Reviewers agreed at journal 03 Dec, 2024 Reviewers agreed at journal 14 Nov, 2024 Reviewers invited by journal 12 Nov, 2024 Editor invited by journal 22 Oct, 2024 Editor assigned by journal 21 Oct, 2024 Submission checks completed at journal 21 Oct, 2024 First submitted to journal 17 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Agency","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Packer","suffix":""},{"id":369802773,"identity":"d5dde8c5-2a0a-49e7-9b4c-a81e3f811387","order_by":2,"name":"Joshua Howkins","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACxgYGNhDBIMEM5H1gSEiAS0kQo4VxBjFagACqBchi5iFGC3N7+7MHjDtsEme28x7+bNuWlifffoD5xcc2hsSZDTgc1nPG3IDxTFribGa+NOnctpxigzMJbJYzgVpm4/LLjBw2Cca2w7nzmHnMmHPbKhI3MCSwGfOcYUich1NL+jOYFuPPlkAt8/sfENKSYAbWMpuZx0CasS0nseFGAvNjngo8Dus5YyaR2JZWP7OZx0yy51xa4oYbD9sYZ1RIGOPyviEwxCQ+ttkYS5w/Y/zhR1ky0GHJhz98MLCRnXEAhxaQUQloNrdJ4ItIeWyCzB9wqh8Fo2AUjIKRCAA2iVrQu4J9FQAAAABJRU5ErkJggg==","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":true,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Howkins","suffix":""},{"id":369802776,"identity":"bb88da92-63d3-46f8-a4ce-f9f46c0953c8","order_by":3,"name":"Carla Robinson","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Carla","middleName":"","lastName":"Robinson","suffix":""},{"id":369802778,"identity":"85dd5bd2-d058-4b79-8a3d-581466827a92","order_by":4,"name":"Theresa Lamagni","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Theresa","middleName":"","lastName":"Lamagni","suffix":""},{"id":369802779,"identity":"840ebf40-c89e-4c6b-a060-f0043c112206","order_by":5,"name":"Alex Bhattacharya","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"","lastName":"Bhattacharya","suffix":""},{"id":369802782,"identity":"bc684ce7-5b41-4f5e-b7d4-6371f1272948","order_by":6,"name":"Rosy Reynolds","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Rosy","middleName":"","lastName":"Reynolds","suffix":""},{"id":369802783,"identity":"e9295347-db0c-4720-8b2e-31ebc7647abb","order_by":7,"name":"Andre Charlett","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Andre","middleName":"","lastName":"Charlett","suffix":""},{"id":369802785,"identity":"fb1fade4-9e72-499d-9cde-3ea8e749fcac","order_by":8,"name":"Colin Brown","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Colin","middleName":"","lastName":"Brown","suffix":""},{"id":369802788,"identity":"eeaf0469-64c6-4b26-afc0-ccc248a1a89d","order_by":9,"name":"Russell Hope","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Russell","middleName":"","lastName":"Hope","suffix":""},{"id":369802790,"identity":"24dca768-6d23-4eab-8f5f-eab2d0d2473b","order_by":10,"name":"Susan Hopkins","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Hopkins","suffix":""},{"id":369802791,"identity":"94f514e4-b283-463e-8115-44351c7cca5e","order_by":11,"name":"Isabel Oliver","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Oliver","suffix":""}],"badges":[],"createdAt":"2024-10-17 15:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5283953/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5283953/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-11539-5","type":"published","date":"2025-09-26T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68167978,"identity":"466c6305-a6b2-4b39-af89-7235733f59b2","added_by":"auto","created_at":"2024-11-04 09:58:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":60541,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of exclusions and data linkage of study dataset with blood isolate episodes.\u003c/p\u003e\n\u003cp\u003eFigure legend: Study dataset contains data from CQUIN, HES, and ONS; blood isolate episodes from SGSS, CDR feed; linked together to create linked study dataset.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5283953/v1/b2330eb06545cced795973c5.png"},{"id":92430593,"identity":"911ddabc-3b9d-40ba-bbbf-cbee294d031e","added_by":"auto","created_at":"2025-09-29 16:06:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1027692,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5283953/v1/6564d999-efbd-46ba-818f-b59be9579890.pdf"},{"id":68167979,"identity":"71e2bcb4-fdec-4dc3-b446-3d65ee6be86a","added_by":"auto","created_at":"2024-11-04 09:58:08","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15933,"visible":true,"origin":"","legend":"","description":"","filename":"Bloodculturepositivesepsispapersupplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5283953/v1/69c13027dae3e205f37f57d8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eBlood culture positive sepsis in England, 2017-2018: epidemiological assessment of the Commissioning for Quality and Innovation (CQUIN) sepsis indicator\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Sepsis is a significant clinical and public health concern due to its high associated mortality, healthcare costs and long term physical, psychological, and cognitive sequelae (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In 2022, sepsis was the reported cause of 3,770 deaths in England and Wales and noted on death certification as being an underlying or contributory factor in a further 25,542 deaths (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Sepsis has a hospital mortality rate of around one-third (36%) (\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRapid identification of patients with sepsis is critical for targeted clinical management (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). It can be clinically screened for using scoring systems such as National Early Warning Score (NEWS2) and others (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, each tool has its own limitations, with variations in clinical coding, case definitions and use in clinical practice. Additionally, as they do not identify the causative organism, they are unable to guide appropriate choice of antibiotics and patients must first be treated empirically (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Therefore, blood culture remains the most important microbiological investigation in the management of sepsis, allowing identification of the responsible organism and direct targeted investigations for underlying source unless this is clinically evident (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). As such, blood cultures drawn before administration of broad-spectrum antibiotics and within 3 hours of a sepsis diagnosis forms a key component of the Surviving Sepsis Campaign care bundle (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is currently no dedicated surveillance system for sepsis in England, limiting our ability to quantify sepsis burden and monitor efforts in reducing its occurrence and impact. Current estimates of sepsis incidence in England are primarily derived from bespoke analysis of routine administrative data, such as Hospital Episode Statistics (HES) using International Classification of Disease (ICD-10) codes to record clinical diagnoses (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This system, which contains details on all admissions at National Health Service (NHS) hospitals in England, is primarily designed to provide financial reimbursement for hospitals, and therefore its secondary use for surveillance has limitations, including imprecise clinical coding of infection (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Additionally, it is often difficult to translate complex conditions, such as sepsis, into a single ICD-10 code, and the use of these codes have previously been found to have low specificity and sensitivity (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). While UK Health Security Agency (UKHSA, formerly Public Health England [PHE]) has developed a national surveillance system to automate the reporting of positive blood isolates from diagnostic laboratories in England, the reason for the blood culture collection is not routinely recorded or collated.\u003c/p\u003e \u003cp\u003eIn 2015 Commissioning for Quality and Innovation (CQUIN) indicators on sepsis were introduced to incentivise its screening in patients that arrive in emergency departments (EDs) and acute inpatient settings (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The CQUIN indicator measured the percentage of adult and child patients arriving in hospital as emergency admissions or on acute in-patient wards who met the NICE criteria for sepsis screening and were screened for sepsis, including review by a senior clinical decision maker, though precise criteria for screening varied by local protocol (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). From 2018, all acute and emergency units were recommended to use the NEWS2 for clinical screening of acutely ill patients; prior to this, local guidance varied widely on the criteria applied for screening patients for sepsis and case definitions used (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). A version of the CQUIN sepsis indicator ran until 2019. The aim was to encourage timely identification and antibiotic treatment, to improve patient outcomes and reduce sepsis-associated mortality and morbidity.\u003c/p\u003e \u003cp\u003eThe data collected on the implementation of the CQUIN sepsis indicator in hospitals provides a novel data source with the potential to further our understanding of sepsis epidemiology in England. It allows a means to further understand and validate other routine data sources, such as HES, for identifying sepsis. Additionally, there is currently limited information available on the pathogens identified in the people treated for sepsis in England. This paper aims to combine data from CQUIN and HES with UKHSA\u0026rsquo;s Second Generation Surveillance System (SGSS) for positive microbiology results, to describe the bacterial and fungal organisms isolated from blood cultures in a sample of patients screened for sepsis in CQUIN and attempt to identify associations between positive bloodstream isolates and clinical outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection and management\u003c/h2\u003e \u003cp\u003eAn existing dataset held by UKHSA was obtained and further supplemented for this study. The original study dataset contained information collated from three data sources: CQUIN, HES and Office for National Statistics (ONS), which had been previously created to assess the feasibility of using these data for sepsis surveillance in England (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Dataset curation is detailed elsewhere (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The dataset contains a random sample of CQUIN sepsis screened admission records from 30 Hospital Trusts, including NHS number, demographic data, and clinical admission data, as well as 30-day mortality result from the ONS linkage. Only individuals screened in, and admitted from, EDs into hospital were included in this dataset. Each record relates to an admission super spell: a continuous period of inpatient care. Persons could be admitted on multiple occasions over the study period. Trusts\u0026rsquo; submissions varied in size and the study used random selection of data submitted by each trust stratified by CQUIN sepsis screen result (positive/negative) with 110 randomly selected in each stratum or, if fewer than 110, all available in that stratum.\u003c/p\u003e \u003cp\u003eIn this study, this dataset was further enhanced through deterministic linkage by NHS number with routine laboratory surveillance data to obtain microbiological results, to create the full linked study dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). First, any records not coded as emergency admissions (i.e., admission to hospital not coming through the emergency department such as elective surgery and maternity) in hospital admission method in HES were dropped. Then, positive isolates were obtained from the Communicable Disease Record (CDR) data feed of the SGSS held by the UKHSA. The CDR feed was queried for all positive blood isolate records (bacteria and fungi) between 25 March 2017 and 05 April 2018 (to allow for a 5-day lag and lead time on specimen date). Isolate records were grouped into rolling 14-day episodes by unique patient identifier (based on NHS number, hospital number, specimen date and date of birth). It should be noted that all positive blood isolates were used, without the ability to differentiate on clinical significance or contamination.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEpisodes were retained where the first isolate record fell within 2 days before and after the patient\u0026rsquo;s hospital admission \u0026lsquo;super-spell\u0026rsquo;, defined in HES as a continuous admission period including transfer to receiving hospitals if applicable, as the infection was considered the same episode of hospitalisation and defined as \u0026lsquo;community-onset\u0026rsquo;. Any cases with a first positive blood culture taken three or more days after the admission the hospital admission were considered \u0026lsquo;hospital-acquired\u0026rsquo; and were not included in the descriptive analysis.\u003c/p\u003e \u003cp\u003eMonomicrobial episodes were classified as those with isolates from a single species within the 14-day period. Polymicrobial episodes were classified as those with multiple isolates of different species, from one or more blood cultures, within the 14-day episode.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe following definitions were employed:\u003c/h3\u003e\n\u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSepsis screen in CQUIN (positive or negative)\u003c/b\u003e: Persons presenting at EDs with suspected sepsis (based on local protocols) who underwent a sepsis screen as part of the CQUIN sepsis indicator and screened either positive or negative for sepsis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSepsis in primary HES code (present or absent)\u003c/b\u003e: Persons included in (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), and who had (present) vs those who did not have (absent) a primary diagnosis of sepsis in the first Finished Consultant Episode from HES (main condition treated during an episode of care). ICD-10 codes A40.x and A41.x were used in this analysis, as used elsewhere (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePositive blood culture (present or absent)\u003c/b\u003e: Persons included in (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), and who had (present) vs those who did not have (absent) a positive blood culture record present within UKHSA\u0026rsquo;s SGSS CDR feed within 2 days before and after hospital admission.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e30-day mortality was determined as a person included in (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) that had an all-cause death recorded in ONS in the thirty days following the date of their admission.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe linked dataset was managed, cleaned, and analysed in R version 3.5.0. Descriptive analyses were undertaken on presence of corresponding positive blood culture results by demographic, admission and clinical variables (obtained from CQUIN, HES, ONS and/or SGSS), and outcome (obtained from SGSS or ONS). The primary outcome was detection of any bacteria or fungi in blood cultures. Categorical variables were compared using chi-squared test and continuous variables compared by Mann-Whitney test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1: Characteristics and outcomes of sepsis screen positives in CQUIN emergency admissions by blood culture results.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32.2259%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive blood culture\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003eSepsis screen positives in CQUIN (n=2454)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003ePresence (n=333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003eAbsence (n=2121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic characteristics, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Male\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1240\u003c/p\u003e\n \u003cp\u003e1214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e190 (15.3%)\u003c/p\u003e\n \u003cp\u003e143 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1050\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;All other ethnic groups combined^\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2159\u003c/p\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e291 (13.5%)\u003c/p\u003e\n \u003cp\u003e21 (14.2%)\u003c/p\u003e\n \u003cp\u003e21 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1868\u003c/p\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eAge (years), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e73 (59-83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e75 (64-84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e73 (58 \u0026ndash; 83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0-17\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;18-49\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;50-69\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;70-79\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;80+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003cp\u003e605\u003c/p\u003e\n \u003cp\u003e572\u003c/p\u003e\n \u003cp\u003e843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19 (10.9%)\u003c/p\u003e\n \u003cp\u003e20 (7.7%)\u003c/p\u003e\n \u003cp\u003e82 (13.6%)\u003c/p\u003e\n \u003cp\u003e82 (14.3%)\u003c/p\u003e\n \u003cp\u003e130 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003cp\u003e523\u003c/p\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003cp\u003e713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary HES code, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eSepsis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Present\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Absent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e665\u003c/p\u003e\n \u003cp\u003e1789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e159 (23.9%)\u003c/p\u003e\n \u003cp\u003e174 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e506\u003c/p\u003e\n \u003cp\u003e1615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical characteristics \u0026ndash;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003esite of infection*, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eRespiratory\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1220\u003c/p\u003e\n \u003cp\u003e1234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e127 (10.4%)\u003c/p\u003e\n \u003cp\u003e206 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1093\u003c/p\u003e\n \u003cp\u003e1028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eGenitourinary\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003cp\u003e2086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e98 (26.6%)\u003c/p\u003e\n \u003cp\u003e235 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003cp\u003e1851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eGastrointestinal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003cp\u003e2376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e18 (23.1%)\u003c/p\u003e\n \u003cp\u003e315 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003cp\u003e2061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eSkin and subcutaneous tissue \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003cp\u003e2289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e36 (21.8%)\u003c/p\u003e\n \u003cp\u003e297 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003cp\u003e1992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003cp\u003e2339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40 (34.8%)\u003c/p\u003e\n \u003cp\u003e293 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003cp\u003e2046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eSeptic shock/SIRS with organ dysfunction\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003cp\u003e2336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e26 (22.0%)\u003c/p\u003e\n \u003cp\u003e307 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003cp\u003e2029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30-day mortality^, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e420\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e65 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43.8538%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9535%;\"\u003e\n \u003cp\u003e2034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9468%;\"\u003e\n \u003cp\u003e268 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2791%;\"\u003e\n \u003cp\u003e1766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.96678%;\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eFigure legend: *as reported in HES; ^as reported in ONS\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDescription of linked study dataset\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf all the 4027 records (CQUIN screen positive or negative), 453 (11.2%) linked to a positive blood isolate within the 2-days before and after their hospital admission (Figure 1): 373 (82.3%) were sepsis screen positive and 80 (17.6%) were sepsis screen negative in CQUIN (Figure 1). Heterogeneity was observed in blood culture positivity rates by CQUIN and HES classifications (Table 2). There was no evidence for a statistically significant difference in 30-day mortality between those with and without presence of positive blood culture (15.5% vs 13.2%, p=0.240) (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBlood culture positivity by sepsis classification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSepsis screen positives in CQUIN had higher rates of positive blood culture as compared to sepsis screen negatives in CQUIN (15.2% vs 5.1%, p \u0026lt;0.001). In addition, heterogeneity was seen within these groups when stratified by presence or absence of sepsis in a primary HES code (Table 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eSepsis screen positives in CQUIN with at least one positive blood culture during admission\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4626%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 42.0965%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive blood culture*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8153%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4626%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresence (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsence (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8153%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4626%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSepsis screen in CQUIN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSepsis in primary HES code\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8153%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4626%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e373 (15.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2081 (84.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8153%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2454\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4626%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6256%;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1298%;\"\u003e\n \u003cp\u003e168 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9667%;\"\u003e\n \u003cp\u003e497 (74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8153%;\"\u003e\n \u003cp\u003e665\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4626%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6256%;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1298%;\"\u003e\n \u003cp\u003e205 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9667%;\"\u003e\n \u003cp\u003e1584 (88.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8153%;\"\u003e\n \u003cp\u003e1789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4626%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e80 (5.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1493 (94.9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8153%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1573\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4626%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6256%;\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1298%;\"\u003e\n \u003cp\u003e18 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9667%;\"\u003e\n \u003cp\u003e110 (85.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8153%;\"\u003e\n \u003cp\u003e128\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4626%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6256%;\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1298%;\"\u003e\n \u003cp\u003e62 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9667%;\"\u003e\n \u003cp\u003e1383 (95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8153%;\"\u003e\n \u003cp\u003e1445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.4626%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6256%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.1298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e453 (11.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3574 (88.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.8153%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eFigure legend: At least one culture result\u0026nbsp;\u003c/em\u003e\u003cem\u003eavailable in SGSS.\u003c/em\u003e S\u003cem\u003eplit by presence/absence of sepsis in a primary HES code (n=4027). Note: *as reported in UKHSA\u0026rsquo;s SGSS\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMicrobiological characteristics by sepsis classification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf the 333 sepsis screen positives in CQUIN with a positive blood culture within two days of hospital admission, 288 (86.5%) were monomicrobial episodes and 45 (13.5%) were polymicrobial. Of the 288 sepsis screen positives in CQUIN with monomicrobial infection, all had bacterial infections (supplementary material). Of these, 140 (48.6%) also had sepsis recorded in a primary HES code. The top five genera identified were \u003cem\u003eEscherichia\u0026nbsp;\u003c/em\u003e(89, 31%)\u003cem\u003e, Staphylococcus\u0026nbsp;\u003c/em\u003e(72, 25%)\u003cem\u003e, Streptococcus\u0026nbsp;\u003c/em\u003e(53, 18%)\u003cem\u003e, Klebsiella\u0026nbsp;\u003c/em\u003e(16, 6%) and \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003e(14, 5%)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eThe top five species were \u003cem\u003eEscherichia coli\u0026nbsp;\u003c/em\u003e(89, 31%), coagulase-negative\u003cem\u003e\u0026nbsp;Staphylococcus\u0026nbsp;\u003c/em\u003e(53, 18%), \u003cem\u003eStreptococcus pneumoniae\u0026nbsp;\u003c/em\u003e(22, 8%), \u003cem\u003eStaphylococcus aureus\u0026nbsp;\u003c/em\u003e(18, 6%) and \u003cem\u003eKlebsiella pneumoniae\u0026nbsp;\u003c/em\u003e(16, 6%) (Table 3). As the numbers of cases were too small to determine pathogen-specific 30-day all-cause mortality, bacteria were grouped into Gram positive or negative. For the 288 records with monomicrobial episodes, there was no evidence of a difference in 30-day mortality amongst those infected with Gram positive vs Gram negative bacteria (16% \u003cem\u003evs\u003c/em\u003e 20% respectively, p=0.57).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 3: Sepsis screen positives in CQUIN with positive blood culture for monomicrobial episodes\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 34.0946%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSepsis in primary HES code\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrganism species name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1289%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e37 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1289%;\"\u003e\n \u003cp\u003e52 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e89 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus (\u003c/em\u003ecoagulase negative)\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e35 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1289%;\"\u003e\n \u003cp\u003e18 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e53 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e10 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1289%;\"\u003e\n \u003cp\u003e12 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e22 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e11 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1289%;\"\u003e\n \u003cp\u003e7 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e18 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e6 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1289%;\"\u003e\n \u003cp\u003e10 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e16 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e5 (3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1289%;\"\u003e\n \u003cp\u003e8 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e13 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus\u0026nbsp;\u003c/em\u003egroup A\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e6 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1289%;\"\u003e\n \u003cp\u003e2 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e8 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cem\u003eEnterococcus faecalis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e4 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1289%;\"\u003e\n \u003cp\u003e3 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e7 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus\u0026nbsp;\u003c/em\u003ealpha and non-haemolytic\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e2 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1289%;\"\u003e\n \u003cp\u003e5 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e7 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 49.2659%;\"\u003e\n \u003cp\u003e\u003cem\u003eMicrococcus luteus (sarcina)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.9657%;\"\u003e\n \u003cp\u003e3 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1289%;\"\u003e\n \u003cp\u003e2 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.6395%;\"\u003e\n \u003cp\u003e5 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eFigure legend: results\u0026nbsp;\u003c/em\u003e\u003cem\u003esplit by presence/absence of sepsis in primary HES code and species (Top 10, n=288)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf the 148 (51.4%) blood culture positive (monomicrobial) sepsis screen positives in CQUIN that did not have sepsis in a primary HES code, the most common HES primary infection ICD-10 codes were for \u0026ldquo;Lobar pneumonia, unspecified organism\u0026rdquo; (12, 13%), \u0026ldquo;Pneumonia, unspecified organism\u0026rdquo; (9, 6%), Pneumonia due to \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e (7, 5%), \u0026ldquo;Pneumonitis due to inhalation of food and vomit\u0026rdquo; (7, 5%), \u0026ldquo;Cellulitis and acute lymphangitis of other parts of limb\u0026rdquo; (7, 5%) and \u0026ldquo;Urinary tract infection, site not specified\u0026rdquo; (7, 5%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Of the 45 sepsis screen positives in CQUIN with positive blood culture results from polymicrobial episodes, all had bacterial infections and made up a total of 98 species isolates (Table 4). Of these, eight cases had three different organisms within one episode, 36 cases had two different organisms within one episode and one case had one organism in their first episode and one (different) organism in their second episode (within the same admission). Of these 98 isolates, the most observed species were \u003cem\u003eEscherichia coli\u0026nbsp;\u003c/em\u003e(16, 16%), coagulase-negative\u003cem\u003e\u0026nbsp;Staphylococcus\u0026nbsp;\u003c/em\u003e(15, 15%), coliforms\u003cem\u003e\u0026nbsp;\u003c/em\u003e(8, 8%), \u003cem\u003eEnterococcus faecalis\u0026nbsp;\u003c/em\u003e(4, 4%), \u003cem\u003eProteus mirabilis\u0026nbsp;\u003c/em\u003e(4, 4%) and \u003cem\u003eStaphylococcus aureus\u0026nbsp;\u003c/em\u003e(4, 4%). None of the sepsis screen negatives in CQUIN had a polymicrobial episode.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 4: Sepsis screen positives in CQUIN with positive blood culture for polymicrobial episodes (Top 10)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73.8333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrganism species name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.1667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73.8333%;\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.1667%;\"\u003e\n \u003cp\u003e16 (16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73.8333%;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus (coagulase negative)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.1667%;\"\u003e\n \u003cp\u003e15 (15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73.8333%;\"\u003e\n \u003cp\u003eColiform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.1667%;\"\u003e\n \u003cp\u003e8 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73.8333%;\"\u003e\n \u003cp\u003e\u003cem\u003eEnterococcus faecalis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.1667%;\"\u003e\n \u003cp\u003e4 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73.8333%;\"\u003e\n \u003cp\u003e\u003cem\u003eProteus Mirabilis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.1667%;\"\u003e\n \u003cp\u003e4 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73.8333%;\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.1667%;\"\u003e\n \u003cp\u003e4 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73.8333%;\"\u003e\n \u003cp\u003e\u003cem\u003eEnterococcus faecium\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.1667%;\"\u003e\n \u003cp\u003e3 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73.8333%;\"\u003e\n \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.1667%;\"\u003e\n \u003cp\u003e3 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73.8333%;\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus alpha and non-haemolytic\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.1667%;\"\u003e\n \u003cp\u003e3 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 73.8333%;\"\u003e\n \u003cp\u003e\u003cem\u003eBacteroides fragilis\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 26.1667%;\"\u003e\n \u003cp\u003e2 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eFigure legend: (n=45 records with 98 isolates)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThere was evidence to suggest that sepsis screen positives in CQUIN that had a site of infection code in HES (such as respiratory) \u0026nbsp;were more likely to have presence of a positive blood culture (p values ranging from p=0.020 to p\u0026lt;0.001) (Table 1), except for respiratory infection where they were less likely to have a presence of positive blood culture (p\u0026lt;0.001) (Table 1).Being male or over 50 years of age were also predictors for presence of a positive blood isolate, as was having sepsis as HES primary code. When looking specifically at sepsis screen positives in CQUIN recorded with respiratory tract as site of infection (n=115), 43 (37.4%) also had sepsis in a primary HES code. The most common species were coagulase negative\u003cem\u003e\u0026nbsp;Staphylococcus\u0026nbsp;\u003c/em\u003e(31, 27%), \u003cem\u003eEscherichia coli\u0026nbsp;\u003c/em\u003e(21, 18%) and \u003cem\u003eStreptococcus pneumoniae\u0026nbsp;\u003c/em\u003e(17, 15%). For records coded with a respiratory site of infection, there was no evidence for a difference in 30-day mortality amongst those infected with Gram positive or negative bacteria, respectively (29% v 18%, p=0.274).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found that of patient admission records with sepsis screens in CQUIN in our linked study dataset, only 11.2% had a positive blood culture taken within 2 days of hospital admission, though note that SGSS only includes positive culture results. Positivity rate varied by both CQUIN and HES sepsis classifications; for instance, sepsis screen positives in CQUIN had higher positivity rate (15.2%) than sepsis screen negatives in CQUIN (5.1%). The positivity rate is lower than previous studies, such as by Bernard \u003cem\u003eet al\u003c/em\u003e who observed 32%, Phau \u003cem\u003eet al\u0026nbsp;\u003c/em\u003ewith 42%, and Panday \u003cem\u003eet al\u003c/em\u003e with ~43% \u0026nbsp;positivity rates (20-22). However, these studies focused on cases defined as severe sepsis \u0026nbsp; or using pre-hospital diagnostic criteria rather than the screening of all potential cases of sepsis within a hospital setting, where sensitivity for sepsis may be prioritised clinically (20-22). We found that sepsis CQUIN screen positives with a noted site of infection code in HES were more likely to have a positive blood culture, except for respiratory disease who were less likely to have a positive blood culture. This may be due to greater clinical certainty of the differential diagnosis when a clear source of infection is observed, except in respiratory infections who may be less likely to become bacteraemic (23).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Most of the blood culture positive episodes identified were monomicrobial, with the most common organisms \u003cem\u003eE. coli\u0026nbsp;\u003c/em\u003e(~30%) and coagulase negative\u003cem\u003e\u0026nbsp;Staphylococcus\u0026nbsp;\u003c/em\u003e(~18%), though note that many of these are likely to be contaminants\u0026nbsp;(24)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eThere was no evidence for a statistically significant difference in 30-day mortality between those with and without presence of positive blood culture. This is in line with Sigakis \u003cem\u003eet al\u003c/em\u003e, who found positive culture was not independently associated with mortality (OR 1.01, 95% CI 0.81 \u0026ndash; 1.26) and Yang \u003cem\u003eet al\u003c/em\u003e, who found positive blood culture was not associated with either in-hospital mortality or 60-day mortality but was associated with increased length of stay in hospital and in Intensive Care Units (p=0.007 and p=0.016, respectively) (25, 26). Conversely, this differs from findings by Panday \u003cem\u003eet al\u003c/em\u003e who found a significant difference in 28-day mortality between culture-positive and culture-negative patient groups (RR 1.43, 95% CI 1.11 \u0026ndash; 1.83) (22). These differences may underscore the importance of independent factors on risk of mortality in sepsis, and the need for further research in this area. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This paper uses a unique multi-site dataset obtained from the CQUIN sepsis indicator, further enhanced by linking together multiple data sources from HES, ONS, and SGSS, allowing an overview of emergency admissions screened for sepsis in CQUIN in England in 2017-2018. This allows for a novel exploration of both the CQUIN sepsis indicator and validation of HES for obtaining information on sepsis, together with detailed description on individual characteristics and main causative bacteria.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;There are several limitations to this study which should be considered. Importantly, we did not exclude any positive culture results which may not have been clinically significant, such as those that may be due to contamination. As such, we may have over-estimated the true rate of positive blood cultures that caused the sepsis episode and impacted the sensitivity of our analysis to observe clinically significant relationships (27). As there was no single definition of sepsis being stipulated in CQUIN, there may have been some heterogeneity in those eligible for screening as part of CQUIN across trusts, which was not possible to assess in this study: as such, we describe individuals throughout the paper as \u0026ldquo;sepsis screen positive/negative in CQUIN\u0026rdquo; to emphasise this. UKHSA\u0026rsquo;s laboratory surveillance system (SGSS) only contains positive blood cultures: as such, for those with an absence of positive blood culture result, it is not possible to ascertain whether this is due to a negative result, perhaps due to preceding antibiotic administration or other factor (28), or to the absence of any culture being taken. By considering all-cause 30-day mortality we may have included deaths due to causes other than sepsis. It was not possible to investigate pathogen-specific 30-day mortality due to low numbers: as such, we categorised organisms into two groups, Gram positive and negative, and compared mortality rates between them. However, the utility of this approach is limited due to the heterogeneity of clinical presentations of organisms within these groups (more details on risk factors associated with mortality can be found in Robinson et al (19)). Finally, administrative systems such as HES and ONS which have their own limitations, such as time of coding relative to screening and diagnosis, and low specificity of coding, which impedes ability to compare these datasets (15).\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur paper highlights the importance of a multi-faceted approach to the identification of sepsis: although positive blood-cultures remain the clinical \u0026ldquo;gold standard\u0026rdquo; in the identification of bacteraemia, other additional markers of sepsis need to be considered as part of hospital admission screening protocols, for prognosis and surveillance. The finding of only 11.2% of admissions having a positive blood culture taken within 2 days of hospital admission is particularly significant here, reinforcing that other markers must play a role in guiding screening, management, and surveillance.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;There is significant local variation in the application of sepsis screening tools: the development of a clinically optimised sepsis-identification pathway, validated across a range of geographic locales and demographic cohorts, is a logical next step towards improving the early identification of sepsis in England, and ultimately reducing 30-day mortality and the societal costs associated with sepsis.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project involved analysis of a patient-level pseudonymised extract from a national linked dataset. Consent to participate was not required. The national linked dataset was approved by the PHE Research Ethics and Governance Group (REGG) on the 25/05/2018, approval number NR012 and by the PHE National Infection Service Caldicott Review Panel on the 15/06/2018. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset analysed during the current study is not publicly available due to inclusion of patient-level pseudonymised data. Data may be available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the NIHR Health Protection Research Unit in Behavioural Science\u003c/p\u003e\n\u003cp\u003eand Evaluation at University of Bristol, in partnership with UK Health Security Agency\u003c/p\u003e\n\u003cp\u003e(UKHSA). The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care, or UKHSA. JH holds an NIHR Academic Clinical Fellowship (ACF-2022-13-013).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIO, SP, TL, AB, CB, RH and SH designed the study protocol. SP obtained ethical approval and participated in data acquisition and linkage. RM performed statistical analyses and drafted the initial manuscript. RR and AC provided support on statistical analyses and TL, CB, and RH with interpretation of results. All authors helped with manuscript revisions. All authors read and approved the final version of the manuscript for publication. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRR and IO acknowledge support from the NIHR Health Protection Research Unit in Behavioural Science and Evaluation at University of Bristol\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3). Jama. 2016;315(8):801-10.\u003c/li\u003e\n\u003cli\u003eCohen J, Vincent J-L, Adhikari NK, Machado FR, Angus DC, Calandra T, et al. Sepsis: a roadmap for future research. The Lancet infectious diseases. 2015;15(5):581-614.\u003c/li\u003e\n\u003cli\u003eOffice for National Statistics. Deaths from sepsis in the UK 2001 to 2022, 2023 [Accessed 16/08/2024]. Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/adhocs/1468\u003cbr\u003edeathsinvolvingsepsisenglandandwales2001to2022.\u003c/li\u003e\n\u003cli\u003eNHS England. Cross-system sepsis action plan 2017 [Accessed 16/08/2024]. Available from: https://www.england.nhs.uk/publication/cross-system-sepsis-action-plan-2017/.\u003c/li\u003e\n\u003cli\u003eDaniels R. Surviving the first hours in sepsis: getting the basics right (an intensivist\u0026apos;s perspective). Journal of antimicrobial chemotherapy. 2011;66(suppl_2):ii11-ii23.\u003c/li\u003e\n\u003cli\u003eVincent J-L, Sakr Y, Sprung CL, Ranieri VM, Reinhart K, Gerlach H, et al. Sepsis in European intensive care units: results of the SOAP study. Critical care medicine. 2006;34(2):344-53.\u003c/li\u003e\n\u003cli\u003eLevy MM, Dellinger RP, Townsend SR, Linde-Zwirble WT, Marshall JC, Bion J, et al. The Surviving Sepsis Campaign: results of an international guideline-based performance improvement program targeting severe sepsis. Intensive care medicine. 2010;36:222-31.\u003c/li\u003e\n\u003cli\u003eRudd KE, Kissoon N, Limmathurotsakul D, Bory S, Mutahunga B, Seymour CW, et al. The global burden of sepsis: barriers and potential solutions. Critical care. 2018;22:1-11.\u003c/li\u003e\n\u003cli\u003eYu SC, Shivakumar N, Betthauser K, Gupta A, Lai AM, Kollef MH, et al. Comparison of early warning scores for sepsis early identification and prediction in the general ward setting. JAMIA Open. 2021;4(3):ooab062.\u003c/li\u003e\n\u003cli\u003eFrankling C, Yeung J, Dark P, Gao F. I spy with my little eye something beginning with S: spotting sepsis. Oxford University Press; 2016. p. 279-81.\u003c/li\u003e\n\u003cli\u003ePatel M. Utility of blood culture in sepsis diagnostics. Journal of the academy of clinical microbiologists. 2016;18(2):74-9.\u003c/li\u003e\n\u003cli\u003eLevy MM, Evans LE, Rhodes A. The surviving sepsis campaign bundle: 2018 update. Intensive care medicine. 2018;44:925-8.\u003c/li\u003e\n\u003cli\u003eGilbert JA. Sepsis care bundles: a work in progress. The Lancet Respiratory Medicine. 2018;6(11):821-3.\u003c/li\u003e\n\u003cli\u003eHospital Episode Statistics (HES) [Internet]. [cited 16/05/2024]. Available from: https://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics.\u003c/li\u003e\n\u003cli\u003eChin Y, Scattergood N, Thornber M, Thomas S. Accurate coding in sepsis: clinical significance and financial implications. Journal of Hospital Infection. 2016;94(1):99-102.\u003c/li\u003e\n\u003cli\u003eJolley RJ, Sawka KJ, Yergens DW, Quan H, Jett\u0026eacute; N, Doig CJ. Validity of administrative data in recording sepsis: a systematic review. Critical care. 2015;19:1-12.\u003c/li\u003e\n\u003cli\u003eNHS England. Commissioning for Quality and Innovation [Accessed 16/08/2024]. Available from: https://www.england.nhs.uk/nhs-standard-contract/cquin/.\u003c/li\u003e\n\u003cli\u003eNHS England. CQUIN Indicator Specification Information on CQUIN 2017/18 - 2018/19 [Accessed 16/08/2024]. Available from: https://www.england.nhs.uk/wp-content/uploads/2017/07/cquin-indicator-specification-information-january-2019.pdf.\u003c/li\u003e\n\u003cli\u003eRobinson C, Packer S, Howkins J, Mulchandani R, Lamagni T, Brown C, et al. Predictors of mortality in emergency admissions screened for sepsis as part of the Commissioning for Quality and Innovation (CQUIN) sepsis indicator: a secondary analysis of a national linked dataset. PREPRINT (Version 1) available at Research Square. 16 October 2024.\u003c/li\u003e\n\u003cli\u003eBernard GR, Ely EW, Wright TJ, Fraiz J, Stasek Jr JE, Russell JA, et al. Safety and dose relationship of recombinant human activated protein C for coagulopathy in severe sepsis. Critical care medicine. 2001;29(11):2051-9.\u003c/li\u003e\n\u003cli\u003ePhua J, Ngerng WJ, See KC, Tay CK, Kiong T, Lim HF, et al. Characteristics and outcomes of culture-negative versus culture-positive severe sepsis. Critical care. 2013;17:1-12.\u003c/li\u003e\n\u003cli\u003eNannan Panday RS, Lammers EM, Alam N, Nanayakkara PW. An overview of positive cultures and clinical outcomes in septic patients: a sub-analysis of the Prehospital Antibiotics Against Sepsis (PHANTASi) trial. Critical Care. 2019;23:1-9.\u003c/li\u003e\n\u003cli\u003eNejtek T, M\u0026uuml;ller M, Moravec M, Průcha M, Zazula R. Bacteremia in Patients with Sepsis in the ICU: Does It Make a Difference? Microorganisms. 2023;11(9).\u003c/li\u003e\n\u003cli\u003eThylefors JD, Harbarth S, Pittet D. Increasing bacteremia due to coagulase-negative staphylococci: fiction or reality? Infection Control \u0026amp; Hospital Epidemiology. 1998;19(8):581-9.\u003c/li\u003e\n\u003cli\u003eSigakis MJ, Jewell E, Maile MD, Cinti SK, Bateman BT, Engoren M. Culture-negative and culture-positive sepsis: a comparison of characteristics and outcomes. Anesthesia \u0026amp; Analgesia. 2019;129(5):1300-9.\u003c/li\u003e\n\u003cli\u003eYang SC, Liao KM, Chen CW, Lin WC. Positive blood culture is not associated with increased mortality in patients with sepsis‐induced acute respiratory distress syndrome. Respirology. 2013;18(8):1210-6.\u003c/li\u003e\n\u003cli\u003eMacGowan A, Grier S, Stoddart M, Reynolds R, Rogers C, Pike K, et al. Impact of rapid microbial identification on clinical outcomes in bloodstream infection: the RAPIDO randomized trial. Clinical Microbiology and Infection. 2020;26(10):1347-54.\u003c/li\u003e\n\u003cli\u003eCheng MP, Stenstrom R, Paquette K, Stabler SN, Akhter M, Davidson AC, et al. Blood culture results before and after antimicrobial administration in patients with severe manifestations of sepsis: a diagnostic study. Annals of internal medicine. 2019;171(8):547-54.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, blood culture, bacteraemia, surveillance, CQUIN","lastPublishedDoi":"10.21203/rs.3.rs-5283953/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5283953/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSepsis remains a significant clinical and public health concern, necessitating timely identification and targeted management for improved patient outcomes. This study describes the epidemiology of sepsis in emergency department attendees across England by analysing a unique multi-site linked dataset to inform approaches to strengthen surveillance and understanding of clinical outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn existent study dataset was utilised comprising a sample of paediatric and adult emergency admissions screened for sepsis in the Commissioning for Quality and Innovation (CQUIN) program in the 2017/18 financial year linked to Hospital Episode Statistics and Office for National Statistics death registrations. This was linked to the United Kingdom Health Security Agency\u0026rsquo;s Second-Generation Surveillance System for microbiological data; descriptive analyses were conducted to characterise sepsis screen positives and negatives in CQUIN, including demographic characteristics, clinical presentations, microbiological profiles, and clinical outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 4027 sepsis-screened emergency admissions included, 2454 (60.9%) were sepsis screen positive under the CQUIN indicator. Only 11.2% had a positive blood culture taken within 2 days of hospital admission. Blood culture positivity rates were 15.2% for sepsis screen positive and 5.1% for screen negatives in CQUIN. Monomicrobial episodes predominated (86.5%), with \u003cem\u003eEscherichia coli\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e species being the most isolated bacteria. The study showed a case fatality rate of sepsis of 17.1% (420/2454) but revealed no significant difference in all-cause 30-day mortality between sepsis screen positives in CQUIN with and without positive blood cultures. However, sepsis screen positives in CQUIN with a focal site of infection code were more likely to have positive blood cultures, except for respiratory infections.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study provides novel insights into the epidemiology of sepsis screening in emergency departments across England, highlighting variability in blood culture positivity rates and microbial profiles. The findings underscore the importance of enhanced surveillance strategies, optimised screening protocols, tailored antimicrobial stewardship practices, and quality improvement initiatives to optimise sepsis management and outcomes. Systemic approaches are needed to address knowledge gaps and inform evidence-based interventions for sepsis care.\u003c/p\u003e","manuscriptTitle":"Blood culture positive sepsis in England, 2017-2018: epidemiological assessment of the Commissioning for Quality and Innovation (CQUIN) sepsis indicator","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-04 09:58:04","doi":"10.21203/rs.3.rs-5283953/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-26T06:36:11+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"213120970825605272194642408050344297988","date":"2025-06-20T00:40:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109358246468697228590376845237330446901","date":"2025-06-17T11:13:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174089187438790732055096198158460185247","date":"2025-06-17T07:25:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193474429827094108306953805015161556266","date":"2025-06-16T19:40:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-16T03:18:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176412363793614644236963056405713744338","date":"2025-06-16T01:19:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-06T00:07:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329022457379053558450814772487346107445","date":"2024-12-03T21:09:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33662330685625870347754516053127830627","date":"2024-11-14T14:32:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-12T10:23:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-22T05:41:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-21T09:07:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-21T09:05:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-10-17T15:13:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f05e5902-ba2a-46a3-9f96-e006a632176b","owner":[],"postedDate":"November 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T16:02:52+00:00","versionOfRecord":{"articleIdentity":"rs-5283953","link":"https://doi.org/10.1186/s12879-025-11539-5","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-09-26 15:57:25","publishedOnDateReadable":"September 26th, 2025"},"versionCreatedAt":"2024-11-04 09:58:04","video":"","vorDoi":"10.1186/s12879-025-11539-5","vorDoiUrl":"https://doi.org/10.1186/s12879-025-11539-5","workflowStages":[]},"version":"v1","identity":"rs-5283953","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5283953","identity":"rs-5283953","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}